The Impact of Digital Infrastructure on African Development The Impact of Digital Infrastructure on African Development

The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. This paper estimates the impact of digital infrastructure on economic growth and its sources. The analysis uses system generalized method of moments and finds evidence of a causal impact from the digital infrastructure variables to economic growth, its sources, income inequality, and poverty. The findings show that mobile connections have an impact on economic growth through the total factor productivity growth channel, while internet users drive it by the capital accumulation channel. Connections have a negative effect on the Gini coefficient, and internet users have a negative effect on the poverty headcount. The analysis also finds that human capital and access to electricity are important complementarities for digital infrastructure to reap benefits. There would be large economic gains if Africa were to close the digital infrastructure gap relative to other regions, yet there are some issues of affordability and skills that need to be addressed to reduce the usage gap and the digital divide across gender, rural-urban, and firm size. This paper is a product of the Office of the Chief Economist, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers The authors


Introduction
Africa houses 1.3 billion people, including the world's largest share of youth. Before 2020, Africa's demography trends projected that by 2030 the continent would account for more than one-third of the world's poor. However, COVID-19 has taken an important toll on those numbers. For the first time since 1998, global poverty will accelerate considerably. Specifically, recent estimations predict that 32 million of the 119 million people pushed into poverty will reside in Sub-Saharan Africa. 2 In this context, the large proportion of young and poor will have an adverse impact on global productivity. Parallelly, inequality could increase by 1 percentage point in the Africa region, as proxied by the Gini Index, discrediting the last three years of progress. 3 Considering the global broad agenda of priorities (health, labor, trade, infrastructure, etc.) to combat the negative externalities from COVID-19, policy makers need to provide solutions that can be built upon. Digital technologies (DT) have proven to have an important impact on development (Aker and Blumenstock 2015). During the year 2020, it was mostly digital technologies that helped the world maintain connectivity and productivity. With DT's, some sectors and factors have increased their efficiency -particularly, healthcare, smart offices, remote assets, location services, and networking technologies. In fact, digital technology adoption reduces a series of economic costs; namely, search costs, replication costs, transportation costs, tracking costs, and verification costs (Goldfarb and Tucker 2019).
First of all, DT's contribute to the alleviation of information asymmetry problems and communication improvement. By enhancing real-time information input -and, thus, transparency-market efficiency increases (Stahl 1989). The speed at which mobile phones spread information can reduce informational barriers for all agents in economic transactions, such as trade or investment (Aker and Mbiti 2010). As information becomes available, demand uncertainty decreases, and the flow of information will also reduce coordination costs i.e. individuals or firms would have a better chance of identifying a market for their products in an expanded area. This is particularly important for those transactions that occur in rural areas (Aker 2011;Debo and Van Ryzin 2013). Studies have shown that some pilot programs have increased access to export markets with technologies such as blockchains.
Particularly, by increasing data transparency across the supply chain and using automatic digital verifications relative to time-consuming paperwork (World Bank 2018).
A second channel through which DTs improve development is by reducing transaction costs (Deichmann et al. 2016). Relative to traditional methods, DTs can be a factor of cost reduction for public and private parties in transfers and payments. Platforms created on handheld devices ease the dynamics and number of intermediate parties involved, thus, the transactions decrease some of the externalities of sending and receiving money and increase the efficiency of public transfer programs (Jack and Suri 2014;. Households are especially benefited due to targeting women in places where there is still an important economic gap (Duflo and Urdy 2004;. Third, the application of digital solutions to finance (say, mobile money, fintech companies) has contributed to grant access to finance for low-income individuals, especially those living in rural areas or the unbanked that have no access to formal institutions, such as commercial banks (Mbiti and Weil 2006). Without financial inclusion, they tend to rely on self-mechanisms that share high risks. Mobile money is a safe option for individuals to deposit savings (Aker and Wilson 2013;Ky, Rugemintwari and Sauviat 2018). In 2018, SSA has around 135 mobile money implementers and 338 million users. 4 Fourth, the use of digital technologies in education can boost human capital by enhancing the provision of content and fostering the acquisition of skills. Individuals can use messaging, or applications to practice reading and writing (Aker, Ksoll, and Lybbert 2002). They can also be used in classrooms as a teaching tool or as substitute teachers.
Finally, DTs create opportunities to attract new entrepreneurs and investors in the market, without discriminating income. Creating and adopting tools that need digital performance can remain relevant when combining the efforts of digital literacy. Specifically, low skilled informal workers can perform better or higher skilled tasks and learn as they work. Without collateral, such workers can increase savings to access credit and insurance products based on recorded savings and purchase histories. Over time, the records can allow them to be matched with better jobs.
Under these circumstances, financial inclusion may help boost productivity growth and reduce poverty. In fragile countries, DTs can be used to manage cross-border movements of displaced people, through digital identities, conduct cash transfers (blockchain) and purchase food from supermarkets (biometric information). Digital psychometric tests can also increase access to loans by eliminating the need for collateral (Alibhai et al. 2018).
The goal of this paper is to statistically evaluate whether digital technologies (DTs) are an important tool for development. More specifically, we examine the impact digital technologies on economic growth, poverty, and income inequality-particularly for the Africa region. To start with, measuring digital technologies is not trivial. It is hard to define because of its multidimensionality. 5 For this paper, we proxied digital technologies with GSMA indicators of digital infrastructure -namely, mobile subscriptions, broadband-capable mobile connections (that is, 3G and 4G connections) and the number of internet users. We estimate the impact of the digital infrastructure on the different developmental dimensions (growth, inequality and poverty) for a panel data of 156 countries with nonoverlapping five-year period observations from 1990 to 2019. To address the likely issues of unobserved effects and weak endogeneity, we use the GMM-IV system estimator for dynamic panel data models (Arellano and Bover 1995, Blundell and Bond) adjusted for small-sample bias (Windmeijer 2005) and overfitting problems due to a large set of instruments (Roodman 2009). Our estimates find that digital infrastructure can foster inclusive growth: it increases economic growth (either through a faster accumulation of physical capital or boosting productivity growth) and it reduces both inequality and poverty.
The paper is divided into 7 sections. Section 1 is the introduction. Section 2 explains the logic behind the driver of growth of the Digital Economy. Section 3 looks at digital infrastructure trends in the Africa region by geography (East and South, West and Central, North Africa, and Fragile, conflict and violent countries). Section 4 empirically estimates the impact of digital infrastructure on economic output, the channels of growth, inequality, and poverty.
Section 5 estimates the potential growth benefits of closing the gap with other benchmark regions. Section 6 is a policy discussion of the digital infrastructure coverage versus the connections. Finally, Section 7 concludes.

The Digital Economy's Drive to Connectivity and Inclusiveness
Harnessing the digital economy requires policies that support a long-term vision to transform Africa's economies, societies, and governments, and unlock new drivers of economic growth, job creation, and poverty reduction. In this context, African governments, development partners, and the private sector must collaborate and commit to policies and programs that stimulate the digital transformation of the continent and allocate resources to building the foundations of a prosperous digital economy. The foundations that would enable African countries to build a dynamic, inclusive, and digitally safe economy comprise: (1) digital infrastructure, (2) digital skills, (3) digital platforms, (4) digital financial services, and (5) digital entrepreneurship. These foundations, in turn, should be supported by a legal and regulatory framework that ensures effective competition, reduces the digital divide, promotes data privacy, and protects against the risks of cybercrime and digital exploitation (World Bank 2019).
The first pillar of the digital economy, digital infrastructure, refers to the network services that are necessary for individuals, businesses, and governments to get online and link with local and global digital services; thus, connecting them to the global digital economy. It includes the spectrum of network, computation and storage functions to successfully operate a connected economy. Digital infrastructure comprises connectivity (through highspeed internet and internet exchange points), the internet of things (mobile devices, computers, sensors, voiceactivated devices, geospatial instruments, machine-to-machine communications, and vehicle-to-vehicle communications), and data repositories (data centers and clouds).
The second pillar, digital skills, are defined as: (a) the ability to enhance the adoption and/or use of digital products and services (digital literacy), (b) the ability to create local content, and (c) the ability to build or run a start-up or enterprise. Digital skills need to be accumulated to drive made-in-Africa solutions needed to ensure an inclusive digital economy, where the region is not only on the consumer side of the digital revolution, but also plays an 6 The fourth pillar, digital financial services, can enable people who are not formally connected to the banking sector to save, make payments, receive remittances, and have access to credit and insurance. Access to financial services can help individuals/households find new ways to make a living, plan for the future, or recover from economic shocks that could affect their incomes (Buckley and Malady 2015). Access to affordable and appropriate digital financial services is critical for the participation of individuals and businesses in the digital economy. Transaction accounts enable people and firms to conduct transactions electronically or online and open a pathway to a variety of digital financial services in addition to digital payments, including credit, savings, and insurance. Firms can leverage digital financial services to transact more easily with their customers and suppliers, as well as to build digital credit histories and seek financing. Governments can use digital financial services to increase efficiency and accountability in different payment streams, including for the disbursement of social transfers and receipt of tax payments. Digital payments are often the entry point for digital financial services and provide the infrastructure through which additional products can be developed, as shown by the evolution of M-PESA in Kenya. A digital financial services ecosystem requires forward-looking and proportionate legal and regulatory frameworks (for example, to allow market entry and innovation), robust financial infrastructures (for example, national payment systems and credit reporting systems), and development and deployment of low-cost delivery channels (agents, point of sale devices, automated teller machines, and mobile phones).
The final pillar, digital entrepreneurship, can help create an ecosystem that brings the digital economy to life.
Innovative digital entrepreneurs can either transform existing businesses or create new growth-oriented ventures that contribute to net employment growth and boost productivity and competitiveness. They offer new products and services that leverage new technologies and business models, and open new markets. A vibrant ecosystem of digital entrepreneurs encompasses skill development (via business mentoring networks), ecosystem-support infrastructure (accelerators, incubators, innovation hubs, and co-working spaces), and access to markets and early-stage financing (seed financing and venture capital). These ecosystems require a conducive and enabling business environment that motivates the creation and use of new digital technologies.
Overall, the digital economy is a multi-dimensional concept. Harnessing the digital revolution implies making sustained investments in the aforementioned five dimensions and putting in place the complementary policies and enabling environment that maximizes the development impact of these investments. For instance, reaping the growth benefits of the digital economy involves the enactment of regulatory frameworks that support digital ecosystems fostering innovation and competition. The goal of this paper is not as ambitious and complex as the concept of the digital economy and does not intend to evaluate the impact of all the different pillars of the digital economy on development. Our approach rather focuses on assessing the impact of the digital infrastructure -that is, the first foundational pillar that also has a larger availability across countries and over time relative to the other pillars.

Benchmarking SSA's Digital Economy in Terms of Infrastructure
This section investigates the trends in digital infrastructure for a sample of 48 Sub-Saharan African countries. It compares the evolution of digital infrastructure indicators in Sub-Saharan Africa (SSA) vis-à-vis other group comparators such as industrial countries and non-SSA developing countries (which we will call henceforth 'developing countries'). Furthermore, it looks at the evolution of the digital infrastructure across country groups in Africa by geographical location -namely, North Africa (AFN), East and Southern Africa (AFE), and Central and West Africa (AFW)-and the group of fragility, conflict, and violence (FCV) affected SSA countries. 8 Assessing the trends in digital infrastructure across countries in the Sub-Saharan Africa region implies examining the evolution of the following indicators over time: a. Total number of connections, excluding licensed cellular internet of things (IoT), as defined by the number of unique SIM cards (or phone numbers, where SIM cards are not used) excluding cellular M2M registered on the mobile network at the end of the period. 9 b. The number of connections by generation, where we distinguish among the spectrum of mobile broadband connections. More specifically, it distinguishes the 2G mobile network coverage (the first-generation offering data services and SMS text messaging) from 3G (first to enable video calls and faster data transfer), and 4G (providing ultra-broadband internet access).
c. Individuals using the internet (as percentage of the population), as defined by the people using the internet (from any location) in the last 3 months. The internet can be used via a computer, mobile phone, personal digital assistant, game console, digital TV, among others.
The data on the number of connections (total and by generation) and percentage of individuals using the internet was collected for a wide array of countries worldwide during the period 1990-2020 (where available The source of data, GSMA intelligence also estimates the number of unique users that have subscribed to mobile services (excluding M2M). Subscribers differ from connections such that a unique user can have multiple connections. This paper uses connections as a better proxy for the supply of digital infrastructure -as unique subscribers may be a better proxy for demand.  There is a great deal of heterogeneity in the penetration of mobile connections across countries in Sub-Saharan

Total Connections 10
Africa. By the end of 2020, 20 of 48 countries have a rate of mobile penetration that exceeds 100 percent (that is, more than one connection per person). 11 The highest rate of penetration is registered by the Seychelles (  3G and 4G connections surged earlier and faster in North Africa than in Sub-Saharan Africa. By the end of 2020, the coverage of the 3G network was more than 4.9-fold that of 2014 in the AFW region, more than 3.4-fold in the AFE region and more than 8-fold among SSA fragile countries. Although the AFE region had a head start and seemed to have the lead on 3G coverage, by the end of 2020, the coverage of the 3G mobile network for the AFW caught up with the AFE region in 2020 at 37 percent. Fragile countries lagged with a coverage of about 27 percent. Finally, the G4 mobile network emerged later across the different African regions (by the second half of the 2010s), and its rate of penetration is still very low. By end of 2020, the coverage of the G4 mobile network is about 10 percent of the population in the AFE region, followed by fragile countries (8 percent), and then the AFW region (7 percent).

Figure 2. Connections in Sub-Saharan Africa and the World by Generation of Mobile Network, 2000-20
(percentage of the population) Finally, there is a greater variation across countries in the coverage of mobile network spectrums across countries in Sub-Saharan Africa. By end of 2020, the average coverage of the 2G network across countries in the region is 37 percent of the population while the coverage of 3G and 4G is equal to 42 and 12 percent of the population. In the case of the coverage of the G2 mobile network, 9 out of 48 countries still have a penetration rate that exceeds half the population, while 6 countries have a rate below 20 percent. There is also a great deal of cross-country variation in the coverage of the 3G mobile network in 2020: 17 countries in the region have a rate of coverage that exceeds 50 percent and 7 countries have a rate of coverage below 15 percent. By end-2020, there were still four countries with no coverage of the 4G mobile network (including Eritrea, South Sudan, among others). In contrast, only six countries in the ratio have a coverage rate of the G4 network that exceeds 15 percent; namely, the Seychelles

A. Sub-Saharan Africa vis-à-vis the World B. Sub-Saharan African country groups
2015-17 (where it grows at an annual average rate of 0.6 percent). Still, 88 percent of individuals in industrial countries had access to internet services by the end of 2017. Developing countries as well as Sub-Saharan African ones exhibit protracted increase in the penetration of internet services across the countries -and, unlike industrial countries, they did not show a deceleration in the expansion of these services. By the end of 2017, the median of internet users as a percentage of population is about 65 percent for developing countries and 20 percent for Sub-Saharan African countries. In spite of the small differences in the levels and trajectory of internet penetration for the AFW and AFE subregions, there is great dispersion across countries-with the 2017 rate of internet penetration fluctuating from 1.3 percent (Eritrea) to 62 percent (Gabon). There are six countries with a rate of internet penetration that exceeds 50 percent of the population (Gabon, the Seychelles, Cabo Verde, South Africa, Mauritius, and Namibia) and about 13 countries with internet penetration below 10 percent in 2017 (including Somalia, the Central African Republic, Chad, Congo DR, among others). Finally, the average rate of internet penetration for the region in 2017 is 24 percent -a level that is comparable to that of Cameroon, Uganda, and Tanzania.

A. Sub-Saharan Africa vis-à-vis the World B. Sub-Saharan African country groups
Note. These figures report the medians across country groups for each corresponding year. Source: WDI

The Impact of Digital Infrastructure: Empirical Analysis
This paper seeks to estimate the impact of the digital infrastructure on economic growth, income inequality, and poverty. Our empirical analysis is carried along the following dimensions: d. We use an estimation method that is suited to dynamic panel data specifications, controls for unobserved time-and country-specific effects, and accounts for the likely endogeneity or reverse causality of the explanatory variables. The method used in this paper is the GMM-IV system estimator for dynamic panel data models developed by Arellano and Bover (1995) and Blundell and Bond (1998). The standard error of GMM-IV system estimated coefficients are adjusted for small-sample bias (Windmeijer 2005) and the proliferation of instruments is also controlled for (Roodman 2009).
Before discussing our empirical analysis, we describe the main features of the estimation method used in this paper.
The GMM-IV system estimator deals with unobserved time effects through the inclusion of period-specific intercepts. Dealing with unobserved countries' effects in dynamic panel data is not trivial (due to the presence of the lagged dependent variable). The method then uses differencing and instrumentation to control for unobserved country-effects and likely endogeneity and reserve causality. Specifically, it allows relaxing the assumption of strong exogeneity of the explanatory variables by allowing the explanatory variables to be correlated with current and previous realizations of the error term. Parameter identification is achieved by assuming that future realizations of the error term do not affect current values of the explanatory variables, that the error term is serially uncorrelated, and that changes in the explanatory variables are uncorrelated with the unobserved country-specific effect. As shown in Arellano and Bond (1991) and Arellano and Bover (1995), this set of assumptions generates moment conditions that allow the estimation of our parameters of interest. respectively. The percentage of internet users has a causal positive impact on growth. When including internet users, our estimates show that 3G plus 4G connections render a greater coefficient estimates than that of each mobile network separately. A 10-percentage point expansion of the 3G plus 4G connections will lead to an acceleration of the economy of 0.41 percentage points (pp), and when estimated separately, 3G connections would render 0.02 and 4G connections would render 0.05 pp. According to the estimates of these specifications, internet penetration increases growth per worker by 0.6 pp per year. Table 2 examines the effect of digital infrastructure on economic growth and its sources; namely, the rate of growth of physical capital accumulation per worker, and the rate of growth of total factor productivity (TFP). Column [1] of  Now, we discuss the estimates on the other determinants other than the digital infrastructure variables that explain economic growth and its sources. First, we find robust evidence of conditional convergence in GDP per worker.

Digital Infrastructure and the Sources of Economic Growth
Countries with lower initial levels of labor productivity tend to exhibit higher growth per worker -and these countries also tend to display greater growth in physical capital per worker and faster TFP growth. Second, growth is fostered by an expansion of education, and this impact is explained by a positive effect on both sources of growth (capital and TFP growth)-although the impact is quantitatively greater on the acceleration of capital accumulation.
Third, government burden has a negative impact on economic growth -and the impact is driven by the acceleration of capital accumulation. Financial depth and governance appear to have an impact of growth that is statistically not different from zero. However, financial depth has a positive impact on physical capital accumulation while governance tends to boost TFP growth. [4] - [6] in which the impact of the connections on growth is driven by capital growth.
The main takeaways from these regressions are: first, digital infrastructure growth has a positive impact on output growth, and it is transmitted through faster accumulation of physical capital per worker and enhanced TFP growth.
Second, the growth returns of an expansion of digital infrastructure are larger in countries that expand broadbandcapable mobile connections (3G and 4G networks) simultaneously. Third, the expansion of mobile connections has a higher impact on economic growth than an increase in the percentage of internet users. Finally, the transmission mechanism for mobile connections and internet penetration is different. The growth effects of expanding connections are transmitted through both faster capital accumulation and enhanced TFP growth, while that of internet penetration is driven only by faster accumulation of physical capital.
So far, we have found that digital infrastructure has a positive and significant causal impact on economic growth, but it is likely that the impact on growth is not homogeneous across countries. This heterogeneity might be the outcome of potential synergies between digital infrastructure and analog complements (such as human capital, and access to electricity) in spurring growth. Table 3 and 4 explore these relationships with the digital infrastructure growth variables. Table 3 follows the specifications from Table 2 Table 3 but estimates the impact of digital infrastructure variables conditional on the levels of access to electricity. Column [1] shows that the interaction of mobile subscriptions and electrical access is not statistically significant. On the other hand, the estimated coefficient of the interaction of electricity access with internet users is positive and significant. However, the positive growth returns take place at very high rates of electricity access (i.e. 98 percent of the population). In columns [2]-[3], only mobile subscriptions has an effect on the accumulation of capital, but not on TFP growth and that internet has no significant relationship with capital accumulation or TFP growth when considering the interaction with electricity access. Table 4 shows that mobile subscriptions has a positive impact on economic growth when electricity access exceeds the threshold of 40% of population. The effect of mobile subscription on output is mainly channeled through capital accumulation, yet when looking at the 3G and 4G connections, growth is transmitted through TFP growth. Under this specification, internet users had no effect on growth through any of its sources. Column [7] which states that mobile subscriptions have an impact on economic growth when electric access is above 65% and the impact is driven primarily through capital accumulation. 4G connections have a significant impact on growth and, analogously to 3G connections, is transmitted through faster capital accumulation. The findings from the last three columns of Table 4 shows that the effect from internet users is transmitted through enhanced TFP growth.

The Inclusive Effects of Digital Infrastructure: Impact on Income Inequality and Poverty
In Tables 1-4 we have found that there are growth benefits from expanding the digital infrastructure. However, it is warranted to ask whether these growth benefits are distributed fairly across the members of society. Do they create opportunities for all? This section evaluates whether the pattern of growth resulting from expanding the digital infrastructure is inclusive by testing its impact on income distribution and poverty. Table 5 presents the regression estimates of the impact of the digital infrastructure on income inequality and poverty-as measured by the Gini coefficient and the poverty headcount, respectively. 17 The first three columns of Table 5  We first discuss the estimates in columns [1] through [3]. Before discussing the findings for our variable of interest (digital infrastructure), it is important to point out that income inequality tends to be lower in countries with higher economic growth, greater rates of secondary enrollment and stronger governance. There is also evidence of a nonmonotonic relationship between the Gini coefficient and the (log) level of income per capita that is consistent with the Kuznets curve hypothesis; that is, income inequality rises and subsequently declines as an economy develops.
On the other hand, our evidence tends to say that greater financial development and urbanization may not have a positive effect on the distribution of income. The effects of the sectoral value added growth on the Gini coefficient differs according to the type of economic activity. Agriculture has no significant relationship with income inequality. In contrast, industrial activity has a positive effect on income inequality while services have a negative impact.
The acceleration of the penetration of mobile subscribers and internet users do not have a significant relationship with the Gini coefficient. However, either 3G or 4G connections have a negative and significant impact on inequality. Column [2] shows that the impact of these broadband-capable mobile connections (3G and 4G together) on inequality is negative and significant, and this impact is higher (in absolute terms) than the separate effect of these as portrayed by column [3]. An increase of 10 percentage points in the expansion of 3G plus 4G connections per capita would lead to a decline in the Gini coefficient of 1.35 percent. Column [3] shows that the impact of 4G connections is more than double than that of 3G connections. The results show that a 10-percentage point increase in 3G and 4G connections would lead to a decrease in the Gini coefficient by 0.04 and 0.09 percent, respectively. This implies that an expansion of the mobile network has a more egalitarian impact on the distribution of income -and this impact is even greater with the arrival of newer technologies.
Next, we discuss the regression estimates in columns [4] to [6] of Table 5. These regressions estimate the effects of digital infrastructure on poverty, as measured by the poverty headcount. Compared to the inequality specifications, these replace the growth rate of agriculture, industry and services for inflation and the share of agriculture in total value added. We include a set of poverty determinants other than digital infrastructure and we find the following: first, countries with higher labor productivity tend to have lower poverty rates. Second, economic growth is good for accelerating poverty reduction. Third, poverty appears to be higher in countries with a larger agriculture sector (as measured by its share in total value added)-although the effect is not statistically significant. Fourth, financial depth will help reduce the poverty headcount; however, the impact is not robust. Fifth, inflation and urbanization have no robust relationship with poverty. Finally, governance has an unexpected positive coefficient. However, it might be the case that the relationship between poverty and governance is non-monotonic.
In a similar fashion that we present our results for economic growth (and its sources) and income inequality, we regress poverty gap on the combinations of the digital infrastructure indicators. Column [4] of Table 5 shows all mobile subscriptions and internet penetration having a negative and significant impact on the poverty headcount ratio. This implies that a faster expansion of the mobile network and an increase in the penetration of internet services can play a crucial role in accelerating poverty reduction. Column [5] and [6] regress poverty gap on mobile subscriptions, internet penetration plus the 3G and 4G spectrums, jointly and separately, respectively. Both columns [5] and [6] show that the mobile-broadband connections technology have no marginal effect on poverty reduction. p-values in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001 NOTE: All digital infrastructure variables are expressed in per capita terms and in log differences.

Potential Growth Benefits of Increasing Digital Infrastructure: A Comparative Statistics Analysis
This section provides an estimate of the economic impact of the expansion of the digital infrastructure variables selected in the regression tables for the Africa region. It relies on the econometric analysis carried in Section 4.2 to conduct a series of counterfactual exercises that provide the potential benefits of accelerating the coverage of the digital infrastructure network on economic growth and its sources (capital accumulation and TFP growth).
The comparative statics analysis conducted in this section consist of first, measuring the gap (or distance) of the digital variable (expansion of mobile subscriptions, 3G + 4G connections, and/or internet users) relative to a select benchmark of Sub-Saharan Africa and sub-regions (AFW, AFE, and FCV country groups) as well as North Africa.
Second, we use the coefficient estimates for each of the digital variables to calculate the economic impact of closing/narrowing the digital infrastructure gap vis-à-vis the selected benchmarks. Hence, we use the regression estimates in column [4] through [6] of Table 2  It is important to highlight that the counterfactual scenarios presented in this section are illustrative rather than conclusive given that, among other simplifying assumptions, they are based on the implicit hypothesis that the expansion of the individual digital infrastructure variables does not lead to changes in any of the other growth determinants. Furthermore, these counterfactual scenarios make no presumption about the desirability, on welfare grounds, of the assumed expansion of the digital infrastructure. More fundamentally, these comparative statics exercises focus only on the development benefits of catching up (as measured by the impact on growth) and ignore the costs it might involve -for instance, the extent of public resources that could be diverted from other uses (say, health, education) in order to support the enhancement of the digital infrastructure network. Such costs can be quite significant and, therefore, these illustrative exercises should be viewed with caution.
Potential Growth Benefits from Digital Infrastructure Table 6 reports the potential growth benefits of accelerating the pace of mobile subscriptions in different African groups (SSA, ESA, CWA, Fragile countries, North Africa, and the whole Africa region) vis-à-vis the reference/benchmark countries/groups. It also reports the channels of transmission of these likely growth benefits by calculating the potential effects on the accumulation of physical capital per worker and total factor productivity (TFP) growth. The reference groups are constituted by the three aforementioned benchmarks-namely, the 95 th percentile for three regions: SSA, the developing world excluding SSA, and EAP. Panel A of Table 6 shows the potential economic growth benefits of reaching the pace of expansion of mobile subscriptions of the different comparator benchmarks. Our estimates suggest that if the SSA region (median) were to catch up the top performers of digital infrastructure in SSA (95 th percentile), the rate of output per worker growth would accelerate by 0.48 percentage points per annum (pppa). The potential growth gains are larger in the AFW region (0.53 pppa) than in the AFE region (0.48) -as they reflect a larger distance to the SSA benchmark for the AFW region when compared to the AFE region. SSA fragile countries-the countries with the slowest rate of expansion of the digital infrastructure-would reap growth benefits of 0.54 pppa. The potential increase of the growth rate for AFN is 0.82 pppa if the subregion were to catch up with the SSA benchmark.
Similarly, the African groups show similar behavior patterns if they are to reach the 95 th percentile performance among the developing countries (excluding SSA) or EAP nations. The potential growth benefits of the SSA median to close the gap with the developing world's would be 1.10 pppa and 0.25 pppa with EAP. The benefits for the AFW region would be around 0.05 pppa larger than for the AFE region. Specifically, the AFW region would render potential output growth benefits of 1.15 and 0.30 pppa if it were to close the gap with the developing world and EAP, respectively. FCV could potentially grow 1.16 pppa if it were to close the gap with the developing world and 0.31 pppa if it were to close the gap with the EAP benchmark.
Panel B of Table 6 looks at the sources of these likely growth benefits by computing the potential increase in the accumulation of physical capital per worker. Our estimates in Table 2 showed that the impact of mobile The behavior of the regions duplicate in the results for TFP accumulation in Panel C Table 6 for all the benchmarks,     SSA and EAP are 23.07,26.51,and 21.51,respectively. The values for Y,K,and TFP are 0.10,0.10,0.03,respectively.

Policy Discussion: Coverage versus Connections 19
The analysis of this paper has so far documented the importance of mobile internet-and, more broadly, digital infrastructure-in driving economic growth, factor accumulation and total factor productivity growth. As documented in this paper, digital technologies can lead to greater aggregate output and productivity growth. At the microeconomic level, the adoption of digital technologies can lead to greater firms' sales, productivity, and better jobs for more people. For instance, the use of e-mail to connect with suppliers or clients, or a business website to have/increase online presence can boost the firm's productivity. On the one hand, these digital solutions expand the group of clients of the firm or increase the number of online transactions of existing clients. On the other hand, businesses with an e-mail account to connect with suppliers expand their potential group of input providers and boost production efficiency (Cusolito, Lederman, and Peña 2020). Finally, greater firm profitability might, in turn, allow firms to invest in innovation, including managerial upgrading, or other types of technology adoption. The empirical evidence points to important benefits from digital technology adoption for firms in Sub-Saharan Africa. 20 Still, the use of the internet for business purposes is also very low in Africa: only 7 percent of the informal businesses surveyed prior to the pandemic in nine Sub-Saharan African countries. The penetration of the internet among informal firms is greater than the average for the region in South Africa (24 percent) and Mozambique (20 percent). 21  having a phone and paying for data services) and schooling (rather than income) are essential for the adoption of mobile services including data. For instance, a 10-year increase in schooling (from 5 to 15) will raise the average probability of using mobile services with data from 8 to 18 percent. Having more friends using data (from 0 to 5) increases the average likelihood of using mobile services with data from 3 to 34 percent. Finally, large increases in income (from top to bottom quintile of the distribution) increases the likelihood of using mobile services without data by only 2 percentage points while having on significant effect on the probability of using mobile services with data (Atiyas and Doganoglu 2021).
Across developing countries, the cost of acquiring data and internet-enabled devices has been declining over time -although at a much slower pace for the latter. Still, it is considerably more expensive in Sub-Saharan Africa (6.8 percent of monthly income per capita) than in other world regions. Within Sub-Saharan Africa, more than threequarters of the countries have affordability levels that exceed the 2 percent target. 23 Developing an inclusive digital economy requires mobile data to be affordable for the poorest. The cost of purchasing mobile data for the poorest segments of the population (say, the bottom 20 percent) is particularly more expensive in Sub-Saharan Africa -at 39 percent of monthly income per capita.
The price of a mobile device as a percentage of personal income is, on average, the highest in Sub-Saharan Africa (69 percent of monthly income per capita) and the lowest in Latin America (16 percent). These prices are even more prohibitive for the poorest segments of the population. On average, the price of an internet-enabled feature phone for the bottom quintile is approximately 81 percent of monthly income per capita in Latin America and 89 percent in East Asia. In Sub-Saharan Africa, the cost of such device among the poorest segments of the population is 375 percent of their monthly income.
The penetration of internet-enabled devices is hampered not only by their high prices but also the lack of payment plans in most post-paid markets in developing countries-and, notably, in Sub-Saharan Africa. Consumers in the region need significant resources, especially those in lower-income households, for one-off purchases. Mobile markets are developing fast in Sub-Saharan Africa and large investments have been undertaken in international connectivity, national backbones, and access networks. However, the adoption of digital technologies by households, firms, and governments in the region still lags that of other regions in the world; and there are large digital divides by urban-rural, gender, and firm size. Further uptake of digital technologies is hindered by limited affordable and quality internet connectivity as well as a lack of widespread availability of adequate digital services to pull demand for digital technologies, coupled with low levels of digital literacy. Therefore, policies are needed across various areas: digital infrastructure for connectivity, digital business models and digital financial services to provide appropriate digital services across economic sectors, public digital platforms and e-government services to create an enabling environment for business and pull demand for digital technologies, and digital skills, gender inclusivity, and capacity to address digital literacy gaps.
More effective regulation is needed to expand digital infrastructure and make connectivity affordable, reliable, and universal. This entails using regulatory instruments and government interventions to eliminate barriers to entry (such as restrictive licensing and exclusivity rights) and promote competition on a level playing field (such as asymmetric regulation of dominant operators, infrastructure sharing, spectrum policy, and antitrust enforcement).
Governments can also implement programs targeted at providing universal access through a combination of various instruments, including universal service funds to partner with the private sector, supply or demand subsidies, and public investments. Government subsidies and other incentives for broadband and mobile providers should be granted to all providers on equal terms without preferential treatment for state-owned enterprises. At the same time, regulatory frameworks in Sub-Saharan Africa should minimize undue regulatory burden on the sector, such as command and control rules that are not capable of accounting for the complexity of regulation and lack of adequate implementation or enforcement. Broadly, regulatory frameworks in the digital sector also need to shift toward a paradigm of fast adoption of technological change and more competitive market dynamics under an agile regulation approach. Hence, there is a need to address the persistence of dominant positions and onerous barriers to market entry, complex and burdensome tax and parafiscal fee schemes, and gaps in regional harmonization.

Conclusions
This paper aims to assess systematically the development impact of one of the pillars of the digital economy-that is, the digital infrastructure. Measuring the size or the capacity of the digital infrastructure network is not trivial.
Assuming that digital infrastructure can be approximated by different indicators-say, mobile connections (with or without broadband capacity) and internet users-this paper provides evidence that digital technologies have the potential to provide Africa with opportunities to unlock new pathways for rapid economic growth, a more egalitarian distribution of income, and poverty reduction. Yet the problem is that access to internet remains limited and not affordable in the continent.
Our empirical analysis is conducted on an unbalanced panel data of non-overlapping five-year observations for 177 countries (including 47 Sub-Saharan African countries) during the period 1990-2019. We run a series of regressions that test the causal impact of digital infrastructure on: (a) economic growth and its sources (i.e. capital accumulation and TFP growth), (b) income distribution (as proxied by the Gini coefficient), and (c) poverty (as measured by poverty headcount). To address the issue of likely endogeneity and reverse causality, this paper uses the GMM-IV system estimator (Arellano and Bover 1995, Blundel and Bond 1998, Windmeijer 2005, Roodman 2009). The main findings of this paper can be summarized as follows.
First, we find that digital infrastructure in the Africa region has been steadily growing but at a much slower pace than in other regions. Within Sub-Saharan Africa, countries with fragile contexts (FCV) have the lowest number of mobile connections or internet users. In terms of the mobile network generation, we particularly find that Africa lags all other regions not only in the surge of mobile connections (across different generations), but also in the declining trend towards obsolescence. For instance, when 2G connections per capita started declining among industrial countries, they continued increasing among SSA countries.
Second, we find a causal positive and significant impact of digital infrastructure on economic growth across countries and over time. The growth returns of investing in digital infrastructure appear to be larger when considering the 3G and 4G connections jointly rather than individually. Additionally, there is evidence of complementarities between digital infrastructure and human capital/access to electricity in accelerating growth. In fact, we find that the growth returns of expanding the digital infrastructure network are higher for countries with greater human capital/electricity access rates.
Third, the impact of an expansion in the digital infrastructure on growth is transmitted through both sources of growth -capital accumulation and TFP growth. Mobile subscriptions, and the combination of 3G and 4G connections per capita have an impact on economic growth primarily through the TFP growth channel, while 3G connections per capita, 4G connections per capita and internet users expand the economy via the capital accumulation channel.
Fourth, there is a causal negative impact of expanding digital infrastructure on the Gini coefficient (our proxy of income inequality) and on poverty (proxied by poverty headcount). In other words, actions that speed up the growth of the digital infrastructure help reduce income inequality and poverty. But the effects are very specific: mobile connections have a significant effect on the Gini coefficient, while that of internet users is not robust, and the effects are the other way around on poverty. Finally, we find evidence of a likely causal impact from digital infrastructure to growth, income inequality and poverty. Accelerating the expansion of the digital infrastructure network in African countries significantly fosters economic growth and helps lower income inequality and poverty. It also finds that complementarities between digital infrastructure and human capital / access to electricity can further ignite faster growth. Still, there is a high percentage of the population that do not have access to mobile connections (especially, broadband-capable connections) as the coverage of mobile networks continue expanding. Reducing the usage gap -along with the digital divide across gender, urban-rural and firm size-requires policies to foster the accumulation of digital skills and greater access to reliable electricity provision. Regulatory frameworks that promote competition and innovation in the telecommunications market can also help address the affordability problems. Overall, reaping the full potential of the digital economy would require the investment in the aforementioned analog complements (skills, electricity, regulation).